Role overview
We are seeking a highly experienced Agentic AI / Generative AI Architect with 15+ years of software and solution architecture experience, combined with hands‑on expertise in Data Science, Machine Learning, and modern agent-based AI systems. This role requires deep technical leadership in designing, implementing, and governing advanced multi-agent AI ecosystems, RAG pipelines, cloud-native AI platforms, and GenAI engineering practices. The ideal candidate will drive end‑to‑end solution architecture for enterprise-grade AI applications while ensuring scalability, robustness, security, and operational excellence.
- Role: Gen AI Architect
- Job Location: Bridgewater, NJ
- Experience: 12 to 15 Years
- *Job Type: Full Time Employment
What you'll work on
- Design & Implement Agentic AI Systems
- Architect and build multi-agent, goal-driven, autonomous AI systems using frameworks such as:
- AutoGen
- LangGraph
- CrewAI
- Create intelligent agent ecosystems supporting orchestration, reasoning, and collaborative task execution.
- Prompt Engineering & LLM Expertise
- Apply advanced prompt engineering techniques including:
- Few-shot prompting
- Chain-of-thought reasoning
- Prompt templates
- Optimize prompt flows for deterministic, scalable LLM-driven systems
- Cloud-Native AI Architecture
- Design and deploy AI/LLM systems on cloud platforms such as AWS Bedrock, Azure OpenAI, Google Vertex AI, etc.
- Ensure solutions meet enterprise NFRs including performance, security, cost-optimization, and availability.
- RAG Pipelines, Vector Databases & MCP
- Architect and deploy RAG pipelines using vector databases such as:
- Pinecone
- Weaviate
- ChromaDB
- FAISS
- Implement MCP Servers and Agent-to-Agent (A2A) communication frameworks.
- LMOPs / GenAIOPs
- Implement end-to-end operational pipelines for GenAI applications including:
- Continuous integration & deployment
- Model monitoring & drift detection
- Logging, observability, and troubleshooting mechanisms
- Establish governance models, reusable patterns, and GenAI best practices.
- Application & Microservices Architecture
- Design microservices-based systems using Spring Boot, REST APIs, and secure API design patterns.
- Implement API security, versioning, and distributed system governance.
- Architect cloud-native applications using AWS/Azure/GCP, Spring Cloud, PCF, or equivalent.
- Collaboration & Leadership
- Work closely with Data Scientists, Product Owners, Business SMEs, and Engineering teams.
- Lead end-to-end solution architecture for enterprise AI initiatives.
- Conduct technical presentations, architectural reviews, and stakeholder communication.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Ai Generative Ai